Many print devices are configured to receive four-dimensional CMYK (cyan, magenta, yellow, and black) signals as input and, therefore, print CMYK colors which are determined from corresponding RGB values. A lookup table is commonly used to convert each digital RGB color signal value to a corresponding digital CMYK value before being received by the printer. Because printers inherently have a complex, nonlinear behavior and therefore have a complex nonlinear colorimetric response even after a printer is calibrated, the full spectrum of CMYK values and printed colors is not a completely accurate representation of the original RGB spectrum. Discrepancies may arise because the relationship between digital values that drive the print device and the resulting colorimetric response is a complex nonlinear function. To deal with this problem, a color correction table is constructed which approximates the mapping between RGB colorimetric space and CMYK values and corrects for nonlinearities and unwarranted absorptions of inks or dyes such that the printer prints the true corresponding color. A color correction table can be as small as 16×16×16 (4096) locations with each location storing a four-dimensional CMYK value. Due to the above-described nonlinear response of the printer, there are areas of the color correction with high curvature that are under-sampled in the table and other smooth areas of the correction that are unnecessarily over sampled. Currently, small color correction tables result in loss of detail and higher delta E (ΔE) accuracy in the darker areas. The term ΔE refers to a measure of color difference, e.g., a difference between a sample color and a reference color in L*a*b* color space. If the amount of image quality loss is too large, higher density correction tables can be employed to reduce the image quality loss.
Presently, to effectively capture the high curvature of the LUT, non-uniformly spaced input RGB levels are selected using an Optimal Node Placement technique. Optimal node placement is performed along each R, G and B channel independently. Node locations are parameterized and the parameters are solved for by reducing the sum of squares error between the high resolution profile LUT and the up-sampled lower resolution LUT. However, parametric optimal node placement forces node placement along a parametric curve and hence, node selection is only sub-optimal. Further, if the nodes on the grid are selected in such a way that they are off-neutral axis, interpolation error can be added for colors along the neutral axis. In final image quality of nonlinear printers, performance of a higher resolution profile LUT (e.g., 70-cube or 100-cube) is often better when compared to a lower resolution profile LUT (e.g., 24-cube or 33-cube), especially in shadows, highlights, flesh tones, sweeps, smoothness, contours, proof matching, neutrality/color balance, memory colors, chromatic colors, contrast, etc., across CMYK and RGB images. Low resolution LUTs are faster in custom profiling by iterating on the printer model in the field. The locations of the nodes in the table become more critical as the table becomes smaller.
Accordingly, what is needed in this art are increasingly sophisticated systems and methods which can capture the curvature of the LUT over the entire multi-dimensional space spanned by the LUT including boundaries and areas inside the LUT to address poor representation of local non-linearities of a printer in order to improve print quality of color reproduction.
Incorporated References
The following U.S. patents, U.S. patent applications, and Publications are incorporated herein in their entirety by reference.
“Optimal Node Placement For Multi-Dimensional Profile LUTS For Arbitrary Media And Halftones Using Parameterized Minimization”, U.S. Patent Publication No. 20090161183
“Iteratively Clustered Interpolation For Geometrical Interpolation Of An Irregularly Spaced Multidimensional Color Space”, U.S. Pat. No. 6,636,628 to Wang et al.
“Refinement of Printer Transformations Using Weighted Regression”, Raja Bala, Proc. SPIE, Vol. 2658, pp. 334-340, (1996).
“Dynamic Optimization Algorithm for Generating Inverse Printer Map with Reduced Measurements”, Sohail Dianat, Lalit K. Mestha, Athimoottil Mathew, Proceedings of IEEE ICASSP (May 2006).
“A Two Dimensional Interpolation Function for Irregularly Spaced Data”, Donald Shepard, ACM National Conference Proceedings, Page 517-524, (1968).
“International Color Consortium—Profile Specification Version 4.2”, (ICC-2004) Profile Format, and Structure.
“A Practical Algorithm For The Inversion Of An Experimental Input-Output Color Map For Color Corrections”, Viassolo, Daniel; Dianat, Sohail; Mestha, L. K; Wang, Yao, International Society for Optical Engineering (SPIE), (March 2003).